10 ways Machine Learning helps Visualize Better Data

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Vandita is a passionate writer and IT enthusiast. She is a Computer Lecturer by profession at the University of Delhi. She has previously worked as a Software Engineer with Aricent Technologies. Vandita writes for MarTech Advisor as a freelance contributor.

Machine learning, when used inside a data visualization environment can explore patterns in underlying data, remove inconsistencies and build more structured and dynamic visual models.

Machine Learning can contribute tremendously to help visualize data better.

Here are 10 ways it helps:

1. Automated Data Processing and Improved Accuracy: The onus is on machine learning algorithms to perform the cumbersome work of sifting through truckloads of data to filter, organize and recognize patterns. With the computational power available in modern systems now, the process is expedited and more accurate. Data is pulled from multiple sources based on real-time interactions and precise and concise information is presented to the user in a visually appealing format.

2. Systematic and Organized: Machine Learning algorithms are designed to mine from historical data and construct better visualization models based on new findings. Using information from diverse sources, machine learning helps structure the data in a more logical and contextually relevant form, thereby presenting a better narrative.

3. Dynamic Data Visualization: Machine learning tools feed on data. The more the data, the better they perform. ML algorithms are designed to improvise their analysis as more data comes in. So if you have continuous data streams, ML will help you visualize all crucial points of your production chain in real-time.

4. Predictive Modeling: Machine Learning can systematically comb through data and identify patterns. ML continuously learns from existing data and applies results to adapt to new information. This helps create accurate visualizations and simultaneously tap into emerging trends and produce real-time predictions. Business no longer has to rely on gut-feel as predictive analysis can aid more informed and data-driven decisions.

Some of the areas where it can identify, process and create data could be:

Customer Defections: identifying brand fallacies and product or service susceptibilities

Even if the staff doesn’t have sophisticated analytical skills, ML will power up visualization tools to translate complicated predictive analysis into a more comprehensible format.

5. Actionable Insights: With unmatched processing and parsing power of machines, visualization software can learn to search for deeper connections with big data sets. Detecting patterns and making predictions can help unlock vital insights stored in large data sets.

For eg.

As soon as you identify abandoning customers, you can use data visualization to understand their behavior, their reason for leaving and decide what you can do to retain them.

If you have information about customer defection, you can react in real-time to activate your retention plan and save a migrating customer.

6. Prescriptive Models: In native stages, ML can support prescriptive models to support sophisticated and intelligent recommendations. This means that the business user doesn’t have to navigate around data as these algorithms present unbiased guidance at key decision points.

7. Helps in Better Decision-making: The vast amount of data lying within complex and different data sources can be rendered easily to identify emerging patterns and new insights taking into account all statistically relevant information. Since the data scientist no longer has to dig deep, he now has time to evaluate potential options rather than spending weeks on analyzing the data first. This makes the decision-making process quick, precise and more informed.

8. Better Personalization: ML can help you understand your customer better not only by tracking them but also by identifying how they engage with your brand on an individual level. Using ML to trace a customer’s journey on a visualization tool can help you identify your customer’s pain points, interest areas and help create a more personalized experience.

9. Detect Anomalies: Apart from detecting emerging patterns, machine learning can help identify outliers which may be a significant point of interest like in fraud analysis. By locating anomalies and generating timely alerts, decisions can be made to avert a potential loss.

10. Build Trust: Machine Learning can help validate the analyst’s predictions and insights thereby adding a new level of trust from decision makers and stakeholders.

Have more to add to this list? Write in to us on editorial@martechadvisor.com